Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model

Mengyao Mei; Lili Ma; Zhihong Liu; Zhongxian Zhu; Jianan Li; Xiaohan Fang

doi:doi:10.11648/j.ijepe.20180703.13

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Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model

Mengyao Mei, Lili Ma, Zhihong Liu, Zhongxian Zhu, Jianan Li, Xiaohan Fang

Published in International Journal of Energy and Power Engineering (Volume 7, Issue 3)

Received: 7 November 2018 Accepted: Published: 8 November 2018

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Abstract

With the continuous increase of China's total energy consumption, we can find the rule and grasp its development trend from the change trend of energy consumption. In order to provide scientific basis for rational use of energy. In this paper,firstly, based on the data of total energy consumption of shandong province from 2007 to 2016, grey prediction model and BP neural network were used to predict total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted value of each year and the average relative error of the two models was 7.25% and 3.70% respectively. Secondly, on the basis of the grey prediction model, BP neural network was used to correct the predicted value of total energy in shandong province. Then, the grey BP modified model was used to obtain the total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted values of each year and the average relative error of the modified model was 2.04%. Finally, the total energy consumption of shandong province in 2018-2035 is predicted. The results show that the average relative error is small and the prediction effect is obvious. This shows that the grey BP model is effective in predicting total energy consumption.

Published in	International Journal of Energy and Power Engineering (Volume 7, Issue 3)
DOI	10.11648/j.ijepe.20180703.13
Page(s)	40-46
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Grey Prediction, BP Neural Network, Grey BP Model, Total Energy Consumption

References

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APA Style

Mengyao Mei, Lili Ma, Zhihong Liu, Zhongxian Zhu, Jianan Li, et al. (2018). Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model. International Journal of Energy and Power Engineering, 7(3), 40-46. https://doi.org/10.11648/j.ijepe.20180703.13

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ACS Style

Mengyao Mei; Lili Ma; Zhihong Liu; Zhongxian Zhu; Jianan Li, et al. Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model. Int. J. Energy Power Eng. 2018, 7(3), 40-46. doi: 10.11648/j.ijepe.20180703.13

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AMA Style

Mengyao Mei, Lili Ma, Zhihong Liu, Zhongxian Zhu, Jianan Li, et al. Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model. Int J Energy Power Eng. 2018;7(3):40-46. doi: 10.11648/j.ijepe.20180703.13

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@article{10.11648/j.ijepe.20180703.13,
  author = {Mengyao Mei and Lili Ma and Zhihong Liu and Zhongxian Zhu and Jianan Li and Xiaohan Fang},
  title = {Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model},
  journal = {International Journal of Energy and Power Engineering},
  volume = {7},
  number = {3},
  pages = {40-46},
  doi = {10.11648/j.ijepe.20180703.13},
  url = {https://doi.org/10.11648/j.ijepe.20180703.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20180703.13},
  abstract = {With the continuous increase of China's total energy consumption, we can find the rule and grasp its development trend from the change trend of energy consumption. In order to provide scientific basis for rational use of energy. In this paper,firstly, based on the data of total energy consumption of shandong province from 2007 to 2016, grey prediction model and BP neural network were used to predict total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted value of each year and the average relative error of the two models was 7.25% and 3.70% respectively. Secondly, on the basis of the grey prediction model, BP neural network was used to correct the predicted value of total energy in shandong province. Then, the grey BP modified model was used to obtain the total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted values of each year and the average relative error of the modified model was 2.04%. Finally, the total energy consumption of shandong province in 2018-2035 is predicted. The results show that the average relative error is small and the prediction effect is obvious. This shows that the grey BP model is effective in predicting total energy consumption.},
 year = {2018}
}

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TY - JOUR
T1 - Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model
AU - Mengyao Mei
AU - Lili Ma
AU - Zhihong Liu
AU - Zhongxian Zhu
AU - Jianan Li
AU - Xiaohan Fang
Y1 - 2018/11/08
PY - 2018
N1 - https://doi.org/10.11648/j.ijepe.20180703.13
DO - 10.11648/j.ijepe.20180703.13
T2 - International Journal of Energy and Power Engineering
JF - International Journal of Energy and Power Engineering
JO - International Journal of Energy and Power Engineering
SP - 40
EP - 46
PB - Science Publishing Group
SN - 2326-960X
UR - https://doi.org/10.11648/j.ijepe.20180703.13
AB - With the continuous increase of China's total energy consumption, we can find the rule and grasp its development trend from the change trend of energy consumption. In order to provide scientific basis for rational use of energy. In this paper,firstly, based on the data of total energy consumption of shandong province from 2007 to 2016, grey prediction model and BP neural network were used to predict total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted value of each year and the average relative error of the two models was 7.25% and 3.70% respectively. Secondly, on the basis of the grey prediction model, BP neural network was used to correct the predicted value of total energy in shandong province. Then, the grey BP modified model was used to obtain the total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted values of each year and the average relative error of the modified model was 2.04%. Finally, the total energy consumption of shandong province in 2018-2035 is predicted. The results show that the average relative error is small and the prediction effect is obvious. This shows that the grey BP model is effective in predicting total energy consumption.
VL - 7
IS - 3
ER -

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Author Information

Mengyao Mei

Department of Electrical Engineering, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Lili Ma

Department of Economic Management, Rongcheng Campus of Harbin University of Science and Technology, Rongcheng, China
Zhihong Liu

Software Engineering Department, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Zhongxian Zhu

Software Engineering Department, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Jianan Li

Department of Electrical Engineering, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China
Xiaohan Fang

Software Engineering Department, Rongcheng College, Harbin University of Science and Technology, Rongcheng, China

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Plain Text BibTeX RIS

APA Style

Mengyao Mei, Lili Ma, Zhihong Liu, Zhongxian Zhu, Jianan Li, et al. (2018). Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model. International Journal of Energy and Power Engineering, 7(3), 40-46. https://doi.org/10.11648/j.ijepe.20180703.13

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ACS Style

Mengyao Mei; Lili Ma; Zhihong Liu; Zhongxian Zhu; Jianan Li, et al. Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model. Int. J. Energy Power Eng. 2018, 7(3), 40-46. doi: 10.11648/j.ijepe.20180703.13

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AMA Style

Mengyao Mei, Lili Ma, Zhihong Liu, Zhongxian Zhu, Jianan Li, et al. Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model. Int J Energy Power Eng. 2018;7(3):40-46. doi: 10.11648/j.ijepe.20180703.13

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@article{10.11648/j.ijepe.20180703.13,
  author = {Mengyao Mei and Lili Ma and Zhihong Liu and Zhongxian Zhu and Jianan Li and Xiaohan Fang},
  title = {Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model},
  journal = {International Journal of Energy and Power Engineering},
  volume = {7},
  number = {3},
  pages = {40-46},
  doi = {10.11648/j.ijepe.20180703.13},
  url = {https://doi.org/10.11648/j.ijepe.20180703.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijepe.20180703.13},
  abstract = {With the continuous increase of China's total energy consumption, we can find the rule and grasp its development trend from the change trend of energy consumption. In order to provide scientific basis for rational use of energy. In this paper,firstly, based on the data of total energy consumption of shandong province from 2007 to 2016, grey prediction model and BP neural network were used to predict total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted value of each year and the average relative error of the two models was 7.25% and 3.70% respectively. Secondly, on the basis of the grey prediction model, BP neural network was used to correct the predicted value of total energy in shandong province. Then, the grey BP modified model was used to obtain the total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted values of each year and the average relative error of the modified model was 2.04%. Finally, the total energy consumption of shandong province in 2018-2035 is predicted. The results show that the average relative error is small and the prediction effect is obvious. This shows that the grey BP model is effective in predicting total energy consumption.},
 year = {2018}
}

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